“People have been trying to detect people through walls since the 70s,” CSAIL professor and lead researcher Dina Katabi told Vice.

“Around 2013, we showed that we can track people accurately. What’s new here is that, for the first time, we can create a dynamic skeleton of the person, their posture, and how they’re moving.”

While humans have spent decades trying to develop technology to see through walls, researchers have made progress in recent years using WiFi signals to track movement through a solid surface.

Until now, the returning signals have been quite scant – but the team at MIT has managed to come up with a new machine learning algorithm that not only improves the signal but also tracks movement with greater accuracy.

Dubbed RF-Pose, the new technology creates a 3D stick-figure representation of a person that moves in sync with their movements. RF-Pose achieves this by sending WiFi signals through the walls and analysing the patterns as they bounce back. Imagine it as an early beta release of a bat’s echolocation system.

Researchers believe the technology has a variety of applications (don’t say surveillance, don’t say surveillance) from video games and police safety to treatment of patients with neurodegenerative diseases like Alzheimer’s or Parkinson’s.

About the author

Filmmaker. 3D artist. Procrastination guru. I spend most of my time doing VFX work for my upcoming film Servicios Públicos, a sci-fi dystopia about robots, overpopulated cities and tyrant states. @iampineros